DocumentCode
1926057
Title
Estimation of Node Localization with a Real-Coded Genetic Algorithm in WSNs
Author
Nan, Guo-fang ; Li, Min-qiang ; Li, Jie
Author_Institution
Tianjin Univ., Tianjin
Volume
2
fYear
2007
fDate
19-22 Aug. 2007
Firstpage
873
Lastpage
878
Abstract
Location knowledge of sensor nodes in a network is essential for many tasks such as routing, cooperative sensing, or service delivery in ad hoc, mobile, or sensor networks, and it is hard to get the precision solution by traditional node localization algorithm, while genetic algorithm is an effective methodology for solving combinatorial optimization problems, so, in this paper, a real-coded version of the commonly used genetic algorithm is described in order to evaluate the precision of node localization problem in wireless sensor networks, meanwhile, the corresponding fitness function and genetic operators are designed. The algorithms presented in this paper are validated on a combined Windows XP and MATLAB simulation on a sensor network with fixed number of nodes whose distance measurements are corrupted by Gaussian noise. The results show that the proposed scheme gives accurate location of nodes.
Keywords
combinatorial mathematics; genetic algorithms; wireless sensor networks; Gaussian noise; combinatorial optimization problems; cooperative sensing; fitness function; genetic operators; location knowledge; node localization estimation; real-coded genetic algorithm; wireless sensor networks; Application software; Cybernetics; Genetic algorithms; Genetic engineering; Global Positioning System; Intelligent networks; Machine learning; Sensor phenomena and characterization; Sensor systems; Wireless sensor networks; Genetic algorithm; Node localization; Positioning systems; Wireless sensor network;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2007 International Conference on
Conference_Location
Hong Kong
Print_ISBN
978-1-4244-0973-0
Electronic_ISBN
978-1-4244-0973-0
Type
conf
DOI
10.1109/ICMLC.2007.4370265
Filename
4370265
Link To Document